用图神经网络预测用户连接，提前优化5G移动性切换
Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach
- 用图神经网络预测用户与基站的连接状态，实现主动切换
- 实验显示模型能捕捉网络动态特性，提升切换效率
- 适合5G O-RAN网络优化和智能移动性管理研究者
移动性能是5G及以上蜂窝网络的关键关注点。为提升切换（HO）表现，3GPP在5G中引入了条件切换（CHO）和第1/2层触发移动性（LTM）机制。尽管这些被动切换策略缓解了切换失败（HOF）与乒乓效应之间的权衡，但常因额外的切换准备导致无线资源利用效率低下。为此，本文提出一种面向O-RAN的主动切换框架，通过用户-小区链路预测识别最优切换目标小区。我们探索了多种图神经网络（GNN）类别用于链路预测，并分析其在移动性管理领域的适用复杂性。基于真实数据集对比两种GNN模型，实验结果表明它们能够有效捕捉蜂窝网络的动态与图结构特性。最后，我们总结研究洞察，并提出未来集成GNN链路预测于O-RAN移动性管理的关键方向。
原文摘要 · Abstract (English)
Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effects, they often result in inefficient radio resource utilization due to additional HO preparations. To overcome these challenges, this article proposes a proactive HO framework for mobility management in O-RAN, leveraging user-cell link predictions to identify the optimal target cell for HO. We explore various categories of Graph Neural Networks (GNNs) for link prediction and analyze the complexity of applying them to the mobility management domain. Two GNN models are compared using a real-world dataset, with experimental results demonstrating their ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key insights from our study and outline future steps to enable the integration of GNN-based link prediction for mobility management in O-RAN networks.
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